Workinging on our automatic detection of ArUCo markers I found this document.
Here is the abstract :
This paper proposes a novel approach for Simultaneous Localization and Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches use natural landmarks (such as keypoints). However, they are unstable over time, repetitive in many cases or insufficient for a robust tracking (e.g. in indoor buildings). On the other hand, other approaches have employed artificial landmarks (such as squared fiducial markers) placed in the environment to help tracking and relocalization. We propose a method that integrates both approaches in order to achieve long-term robust tracking in many scenarios.
Our method has been compared to the start-of-the-art methods ORB-SLAM2 [1] and LDSO [2] in the public dataset Kitti [3], Euroc-MAV [4], TUM [5] and SPM [6], obtaining better precision, robustness and speed. Our tests also show that the combination of markers and keypoints achieves better accuracy than each one of them independently.
1902.03729.pdf (4.2 MB)
I believe that automatic detection of GCPs is more and more common this days (I still hope to find the time to propose a stage to include ArUCo detection directly in ODM sometimes) so it could interest some people to look at this.
